LLXml

LLXml is a hypothetical markup language designed for representing and exchanging large language model (LLM) related data. It aims to provide a structured and standardized format for describing various aspects of LLMs, including their architecture, training data, parameters, and inference results. The language is intended to facilitate interoperability between different LLM frameworks and tools, making it easier to share, reproduce, and build upon LLM research and applications.

The core idea behind LLXml is to define a set of tags and attributes that can capture the complexity of LLMs in a machine-readable way. This might include elements for specifying model dimensions, layer types, activation functions, optimizer settings, and dataset characteristics. For inference, LLXml could represent prompts, generated outputs, attention weights, and other relevant metrics. The use of XML as a base provides a familiar and extensible structure, allowing for the addition of new elements as LLM technology evolves.

While LLXml itself is a conceptual framework, its development would involve defining a formal schema or DTD (Document Type Definition). This schema would act as the blueprint for valid LLXml documents, ensuring consistency and enabling automated validation and processing. Developers could then create parsers and tools that understand LLXml, allowing for seamless integration into existing LLM workflows. The ultimate goal is to reduce the friction associated with managing and communicating LLM-specific information, thereby accelerating progress in the field.